scholarly journals Time-cost optimization using harmony search algorithm in construction projects

2019 ◽  
Vol 18 (2) ◽  
pp. 226-237
Author(s):  
Mohammad Lemar Zalmai ◽  
◽  
Cemil Akcay ◽  
Ekrem Manisali ◽  
◽  
...  
Keyword(s):  
Construction Projects ◽  
Search Algorithm ◽  
Cost Optimization ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Time Cost

scholarly journals CO2 and Cost Optimization of Reinforced Concrete Cantilever Soldier Piles: A Parametric Study with Harmony Search Algorithm

2020 ◽  
Vol 12 (15) ◽  
pp. 5906 ◽  
Author(s):  
Zülal Akbay Arama ◽  
Aylin Ece Kayabekir ◽  
Gebrail Bekdaş ◽  
Zong Woo Geem
Keyword(s):  
Co2 Emission ◽  
Search Algorithm ◽  
Cost Optimization ◽  
Harmony Search ◽  
Minimum Cost ◽  
Objective Evaluation ◽  
Harmony Search Algorithm ◽  
Parametric Modelling ◽  
Cross Sectional ◽  
Design Variables

This paper presents the parametric modelling process of cantilever soldier pile walls based on CO2 and cost optimization with the Harmony Search Algorithm. The study attempted to fulfil the geotechnical and structural design requirements and sustainable usage necessities simultaneously. The variants of the optimum design process are selected as the cross-sectional characteristics of cantilever soldier piles such as the length and diameter of the pile, and the other design variables are the reinforcement detailing of the pile such as the diameter and the number of reinforcement bars. Besides the volume of the concrete, the unit prices of both reinforcement and concrete are evaluated as another part of the variants. The shear and flexural strength necessities, minimum cross section of the reinforcing bars and factor of safety values are identified as the constraints of the optimization. Different objective functions are defined to provide the minimum cost, the minimum CO2 emission and the integrated multi-objective evaluation of cost and CO2. In addition, the type of steel and concrete reinforcement on the optimum CO2 emission is investigated with the use of different material emission values that are selected from current literature studies. Consequently, the results of the optimization analyses are interrogated to investigate if the attainment of both minimum CO2 and cost balance can be achieved.

Optimal linear cooperation spectrum sensing method based on chaos harmony search algorithm

2013 ◽  
Vol 32 (9) ◽  
pp. 2412-2417
Author(s):  
Yue-hong LI ◽  
Pin WAN ◽  
Yong-hua WANG ◽  
Jian YANG ◽  
Qin DENG
Keyword(s):  
Spectrum Sensing ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Optimal Linear

Performance Study of Harmony Search Algorithm for Multilevel Thresholding

2016 ◽  
Vol 25 (4) ◽  
pp. 473-513 ◽  
Author(s):  
Salima Ouadfel ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Performance Study ◽  
Bacterial Foraging Optimization ◽  
Bee Colony ◽  
Fitness Value ◽  
Multiple Thresholds

AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel thresholding in an image are selected by maximizing three criteria: Between-class variance, Kapur and Tsallis entropy using harmony search (HS) algorithm. The HS algorithm is an evolutionary algorithm inspired from the individual improvisation process of the musicians in order to get a better harmony in jazz music. The proposed algorithm has been tested on a standard set of images from the Berkeley Segmentation Dataset. The results are then compared with that of genetic algorithm (GA), particle swarm optimization (PSO), bacterial foraging optimization (BFO), and artificial bee colony algorithm (ABC). Results have been analyzed both qualitatively and quantitatively using the fitness value and the two popular performance measures: SSIM and FSIM indices. Experimental results have validated the efficiency of the HS algorithm and its robustness against GA, PSO, and BFO algorithms. Comparison with the well-known metaheuristic ABC algorithm indicates the equal performance for all images when the number of thresholds M is equal to two, three, four, and five. Furthermore, ABC has shown to be the most stable when the dimension of the problem is too high.

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